Individual phenotypic variations exist ubiquitously in biology. Much evidence shows scarce cells can determine the fate of a population. Identifying and studying such a small group of key individuals from naturally occurring environmental microorganisms remains a significant challenge, since the vast majority of them remain “dark matter”. Even for cultivable microorganisms in the laboratory, individual difference is often masked by conventional methods that use the average response from a population. In this context, our work has focused on integration of microfluidic platforms with advanced imaging and Raman spectroscopic technologies for single cell analysis and sorting. Our approaches enable quantitative and real time analysis of individual cells within a population, without the need for extrinsic and external labelling processes.

Here, I will illustrate these approaches with two recent examples: 1) An automated Raman activated single cell sorting system that allowed us to continuously sort individual cells directly from their native fluids (e.g. microbial communities from the ocean) based on their physiological activity. 2) A quantitative single-cell growth platform that reveals a diversity of individual growth behaviors in a population under well-controlled stress microenvironments. These platforms enabled us to reveal hidden key individuals and their roles in the survival and function of a population, and helped develop a better understanding of bacterial responses to environmental challenges. As such, they provide a powerful tool for microbiology research with many potential applications in environmental science, synthetic biology and drug discovery.